Chatbots are rapidly transforming digital marketing strategies in direct to consumer industries including fashion & apparel, ecommerce, retail, and automotive. They are being adopted for guided shopping experiences, lead generation, and scaling personalized experiences from the first ad touchpoint. 

Conversational marketing chatbots streamline customer communication and acquisition marketing funnels. They develop meaningful relationships by building trust with messaging and giving people exactly what they want, when they want it.

16% of marketing and sales departments have already adopted AI-driven chatbots. There is still plenty of room to adopt them as a marketing channel and gain a competitive advantage.

Departments adopting AI chatbots

How do you know if a marketing chatbot is generating a positive return investment? How do you know where it can be improved and what to expect performance to be in the future? Through marketing chatbot key performance indicators (KPIs).

These are 9 chatbot KPIs that your brand can measure and optimize to maximize the success of conversational marketing chatbots. They begin from the first brand interaction a customer has with your business to the final conversion, including:

  1. Brand Interactions per User
  2. Customer Insights per User
  3. Chatbot CTR to Website
  4. Matched Response Rate
  5. Cost per Conversion
  6. Conversion Rate
  7. Return On Ad Spend (ROAS)

Chatbot KPI 1: Brand Interactions per User

How often are customers interacting and speaking with your marketing chatbot during a given time period? This is determined by measuring brand interactions per user. Interactions are defined as any active engagement a user has with your brand via the chatbot. That includes a click on a quick reply chip, button, carousel call to action, or sending a message. It does not include things like seeing messages or visuals.

Brand Interactions per User = Total Interactions / Total Users

For example, if your marketing chatbot generates 25,000 interactions from a total of 10,000 users that engage with it, that equals 2.5 brand interactions per user.

The number of brand interactions per user is important to measure as it helps to measure the depth of engagement with your marketing chatbot within a single engaged conversation and over longer time periods. 

Measuring the brand interactions per user helps improve marketing chatbot performance and customer experience over each iteration. Every touchpoint from discovery to conversion can be optimized. This reduces the amount of friction a customer experiences while learning about your brand and purchasing a product. It gives you valuable insight into where and how often a chatbot is being engaged with.

Chatbot KPI 2: Customer Insights per User

Each brand interaction with a chatbot provides declared data which can be leveraged to improve the performance of the chatbot and other marketing campaigns. This is information volunteered by the consumer during a conversation. Declared data is valuable because it empowers brands to stop relying on hunches. Ideas. Assumptions. Declared data can be used to validate ideas and accurately identify a customer’s wants, needs, and desires.

How much data you generate from each customer is equally important. This KPI is called customer insights per user. It is calculated by dividing the total declared data points by users for a given time period. 

Customer Insights per User = Total Declared Data Points / Total Users 

For example, if your conversational marketing chatbot collects 250 total declared data points out of 5,000 users, that is 5 customer insights per user. 

Deep customer insights can be collected using Spectrm’s conversational marketing platform. You are able to define attributes and values based on the responses your chatbot collects. Apply taxonomies for each attribute and assign them to responses. Over time, this creates a treasure trove of customer data that can be leveraged for segments, recommending tailored products, and other actionable insights at scale.

Chatbot KPI 3: Chatbot CTR to Website

Conversational marketing chatbots are an effective channel for driving conversions and sales. Chatbots should be designed to guide customers through every stage of a marketing funnel to conversion. The click-through rate of a conversation is critical because of this. Higher CTR results in more qualified traffic going to key assets like product pages. Chatbot CTR measures the number of clicks to your website as a percentage of people who engaged with your chatbot.

Chatbot CTR to Website = Total Clicks / Total Users x 100

For example, if a marketing chatbot generated a total of 1,000 clicks to your website out of 8,000 users, the click-through rate would be 12.5%.

How can you increase the CTR of a conversational marketing chatbot? Firstly by setting conversation goals. Every marketing funnel has stages and goals. Marketing chatbots are no different. They allow your brand to see how effective a chatbot is at driving the action you want customers to take. What conversation goals do you have and what can customers do to achieve them? Here are some examples of goals you can set:

  • Each response you collect during steps in a guided journey.
  • The quick reply button you use to generate a product feed.
  • The clicks on a product carousel item to take someone to a product page on site.

Optimizing conversations for click-through rate is essential for increasing the return on investment of marketing chatbots. You can track engagement using a tool like Spectrm’s conversational marketing platform. A/B test conversation copy and creative to determine combinations that drive the best CTR.

Monitor conversational goals on a regular basis. Notice drop offs due to freeform messages? Add more intent templates based on the data you collect. The conversational AI will improve the more messaging data it receives from customers and keep people in your conversation funnel. 

Continually review your chatbot analytics to identify opportunities for improving CTR. See what creatives and copy customers engage with the most. Test new calls to action. Small tweaks can have big impacts on performance. Reviewing your chatbot analytics on a regular basis will help you continuously improve CTR, conversion rates, and performance.

spectrm conversation designer

You should also use clear calls to action such as “Buy now” or “Learn more” to prompt engagement. In particular, use verbs. These are action words and phrases that push the customer to the next step. 93.67% of calls to action use verbs like:

  • “Buy”
  • “Visit”
  • “Find”
  • “Try”
  • “Schedule”
  • “Discover”
  • “Browse”
  • “View”
  • “See”

Chatbot KPI 4: Matched Response Rate

Each customer’s intent is incredibly different. They all have unique wants, needs, and demands. Unfortunately, many chatbot platforms aren’t capable of accurately mapping responses to tailored suggestions. They utilize generic approaches to natural language processing (NLP), making them unable to deliver responses and information tailored to their exact situation.

Matched response rate is the measurement of how often a marketing chatbot accurately matches customers with what they ask for. This KPI is only possible of being analyzed with marketing chatbots driven by machine learning and NLP such as ones built with Spectrm, which is designed to help you as a marketer easily train your own conversational AI to support your chatbot performance.

Matched Response Rate = Matched Responses / Total Messages x 100

For example, if a conversational marketing chatbot accurately matched 100 messages out of 200, the matched response rate would be 50%.

NLP-powered chatbots identify the most important things that customers are saying. They can determine specific intents in different contexts. Marketers can train them to continually make better predictions and match suitable responses. This can be relevant information or product catalogues in real-time. This is critical as 80% of consumers are likely to purchase from a brand that personalizes experiences.

FAQs, which you may already have, are one way to improve matched response rate. These are questions about products, shipping, and returns. Use this content to anticipate questions and match responses more appropriately. Leverage customer input and data to create more accurate templates over time.

Matched response rate can also be increased by offering tailored product and content recommendations. Suggestions that adapt to a customer’s unique needs, wants, and desires. Not what everybody wants. 

Reviewing messages is important for training chatbots and developing brand-specific conversational AI. Training chatbots improves customer experience by being able to engage in more situations. It will identify complex customer intents and respond in real-time with relevant information. You can use Spectrm’s conversational marketing platform to train a bot’s conversational AI by:

  1. Validate matches with customer responses to create accurate templates.
  2. Create new templates with the incoming messages your chatbot receives.
  3. Add incoming messages to existing templates.

Chatbot KPI 5: Cost per Conversion

Cost per conversion is the total cost of acquiring a customer or of a customer completing a specific goal depending on your campaign. This includes things like making a purchase, watching a video, filling out a form, or scheduling a meeting. Since marketing chatbots drive conversions through guided shopping and personalized experiences, it’s important to know the cost of each conversion action. This empowers your brand to discover ways to decrease cost per conversion while generating revenue.

Cost Per Conversion = Total Cost of Generating Traffic / Total Conversions

For example, if the total media spend on a conversational display ad campaign was $5,000 and it resulted in 100 conversions, the cost per conversion would be $50.

Conversions and their costs can be tracked using UTM parameters on URLs in your bot. These are snippets of text that can be added to the end of a URL to track conversions on your website using your analytics tools, such as Google Analytics.  

For example, you can use this UTM campaign builder to add parameters to URLs and track them. Enter a website URL with a campaign source, medium, and name.

UTM generator website (1)

Tracked URLs will then appear under “Source/Medium” in Google Analytics reports.

Chatbot KPI 6: Conversion Rate

Traffic, clicks, and engagement are great. But they need to lead to conversions. The conversion rate of a marketing chatbot is the rate at which traffic from your traffic chatbot converts on your website. These include a completed purchase, reaching a particular page, or scheduling an appointment. It’s calculated by dividing the number of conversions by the total amount of traffic and multiplying it by 100 to get a percentage.

Conversion Rate = (Conversions / Total Visitors from Chatbot) x 100

For example, if you generate 100 conversions from 2,000 total visitors from your Facebook Messenger chatbot, that would equal a 5% conversion rate.

Tracking your conversion rate is how you understand how marketing chatbots perform compared to other marketing channels you are using. It is a key performance indicator for your chatbot.

Play Video

Telekom, the global communications company founded in Germany, struggled to give consumers an effortless way to find personalized phone contracts. They wanted to give people an easier solution as the amount of phones and data bundles were overwhelming. Telekom built a contract finder on Facebook Messenger using Spectrm’s conversational marketing platform.

It provides millennials with phone contracts based on their individual preferences. This helped Telekom engage customers at scale and recommended relevant products. The conversational contract finder generated a 93% contract conversion lift vs their website traffic campaigns on Facebook. Evaluating this lift in conversion rate is only possible when you are tracking the conversion rate of both your chatbot traffic and your traffic from other campaigns. 

Learn more about how Telekom used Spectrm’s conversational marketing platform to increase conversions in the full case study here.

Chatbot KPI 7: Return on Ad Spend (ROAS)

Conversational marketing chatbots built with Spectrm integrate with platforms your customers are already active on. These include Facebook Messenger, Instagram, and Google DV360. 

Since marketing chatbots and ads go hand in hand, it’s important that your brand measures return on ad spend (ROAS). This is determined by calculating how much revenue is earned for every dollar spent on ads connected to your marketing chatbot.  ROAS calculations can get very complex, but they can also be very straightforward. Here is what the ROAS formula looks like for marketing bots:

Return on Ad Spend = Total Value of Conversions / Total Ad Spend

For example, if you sell $1,000 worth of products attributed to your Facebook Messenger bot after spending $200 on click-to-Messenger ads, the return on ad spend is 5x.

Play Video

Happy Socks, the Swedish fashion retailer, was able to generate a 3.3x return on ad spend with a guided selling chatbot built with Spectrm. It helped customers navigate over 300 sock styles to find the perfect pair for a loved one on Valentine’s Day. This also increased revenue by 20% and helped Happy Socks engage customers at scale and build meaningful connections with them.

Learn more about how Happy Socks used Spectrm’s conversational marketing platform to increase ROA in the full case study here.

Another example of the potential ROAS of marketing chatbots is Purple, the D2C mattress brand. It created a “Mattress Finder” chatbot built with Spectrm’s conversational marketing platform. It reached 90% of internet users through Google’ network of audiences to prospect new customers and educate people about Purple’s unique benefits.

The AdLingo campaign generated a return on ad spend 30–40% higher than Purple’s original goal. With the success of the chatbot, Purple has continued to invest in conversational ads as a fundamental way to reach new customers.

Learn more about how Purple drove over $100,000 in revenue with Spectrm’s conversational marketing platform in the full case study here.

Other conversational marketing chatbot KPIs

These are other key performance indicators your brand can track to determine the success of a marketing chatbot.

Ad recall

Customers are bombarded with ads every day. Advertisements that aren’t tailored to them. Ones that don’t resonate or offer any hint of personalization. The result is banner fatigue. Customers are less likely to engage with ads and also forget them in an instant. 

Advertisements integrated with a conversational marketing chatbot can have better ad recall. They create memorable and remarkable experiences. Conversations they will tell their friends and family about. This can also increase brand loyalty and recognition.

Coppafeel, a breast cancer awareness charity, wished to give people the confidence to follow a breast cancer check-up routine. It was difficult to do in person and changing consumer’s health habits was already difficult. People are looking for reassurance. Help every step of the way. 

As a solution, Coppafeel launched a conversational health assistant on Facebook Messenger with Spectrm’s conversational marketing platform. It educates young people to be breast aware and sends them regular health reminders. The unique campaign generated a 3x ad recall lift, 30% action intent lift, and 22% subscription rate.

Learn more about how Coppafeel increased ad recall using Spectrm in the full case study here.

Purchase intent

Purchase intent is the likelihood of a customer purchasing a product. It’s extremely useful to measure when expanding into new industries or countries. Marketing chatbots can be used to determine and increase purchase intent in these situations.

Purchase intent can be complex to measure and depends on your approach to modelling it. However, if you have a way to model purchase intent, chatbots can be integrated into this.

Ford, the leading automotive company, increased purchase intent by 38% when entering Thailand by creating a conversational AdLingo campaign. The assistant, built with Spectrm, engaged prospects and gave them personalized information about Ford’s new Ranger model. 

This helped Ford stand out from the competition, increase demand for their vehicle, and capture new leads. You can learn more about how Ford increased purchase intent with a marketing chatbot in the full case study here.

The bottomline on marketing chatbot KPIs

Key performance indicators are extremely important. Without them, your brand won’t know how a marketing chatbot is performing. You won’t know where it can be improved. Marketing efforts will be in the dark. Use the main chatbot KPIs we covered along with other metrics like ad recall and purchase intent to help your business measure performance and achieve its goals. 

Here’s a recap:

  1. Brand interactions per user
  2. Customer insights per user
  3. CTR to web page
  4. Matched response rate
  5. Cost per conversion
  6. Conversion rate
  7. Return on ad spend

Contact one of our experts today to learn how your brand can scale with Spectrm’s conversational marketing platform.

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